We implement two case studies (i.e. heavy and light load cases) based on the IEEE 33-bus system [1]} (shown in Fig. REF ) to demonstrate the benefits of the proposed framework. The heavy-load case is used to validate the optimality and reliability of the proposed MLP-based framework, while the light-load case is implemented to show the benefits of treating building thermal inertia as flexibility source. We assume PV power stations are installed at 5 different buses (i.e. buses 6, 9, 12, 18 and 30). Note that the information of topology and parameters of the IEEE 33-bus system are only used to generate training data and assumed to be unknown in the view of the proposed framework. The total heat load \(\mathbf {q}_t^h\) , ambient temperature \(\theta _t^\text{out}\) , active based load \(\mathbf {P}_t^e\) , reactive base load \(\mathbf {Q}_t\)  and available PV generation \(\mathbf {G}_\text{av}^\text{PV}\)  are illustrated in Fig. REF . All loads in the heavy-load case are the same with those in Fig.REF , while the loads are reduced by half in the light-load case.
The optimization time horizon is 24 hours. The values of other parameters are summarized in Table REF .
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